class_docstr: Linear layer in Factorized Noisy Network
  
  Args:
      mu_link (nn.Linear): Linear link that computes mean of output.
      sigma_scale (float): The hyperparameter sigma_0 in the original paper.
          Scaling factor of the initial weights of noise-scaling parameters.
code: def _eps(self, shape, dtype, device):
      r = torch.normal(
          mean=0.0, std=1.0, size=(shape,), dtype=dtype, device=device
      )
      return torch.abs(torch.sqrt(torch.abs(r))) * torch.sign(r)
  
file_path: pfrl/pfrl/nn/noisy_linear.py
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the FactorizedNoisyLinear obj's underlying member method code with example usage: obj._eps(shape, dtype, device) and returns: unknown